Paper Info
Title
Getting adaptability or expressivity in inductive logic programming by using fuzzy predicates.
Abstract
Introducing fuzzy predicates in inductive logic programming may serve two different purposes : getting more expressivity by learning fuzzy rules, or allowing for more adaptability when learning classical rules. On the one hand, we can thus learn gradual and certainty rules, which have an increased expressive power and have no simple crisp counterpart. On the other hand, fuzzy predicates in rules can be used for dicretization when the database contains numerical attributes. In this case the fuzzy counterparts of crisp rules allow us to check the meaningfulness and the accuracy of the crisp rules. In this paper we formally describe the computation of the confidence degrees for each type of rules with fuzzy predicates. Next, we discuss the interest and the application domain of each kind of rules with fuzzy predicates.
Year
DOI
Venue
2004
10.1109/FUZZY.2004.1375691
FUZZ-IEEE
Keywords
Field
DocType
fuzzy set theory,inductive logic programming,learning (artificial intelligence),fuzzy predicates,inductive logic programming,learning fuzzy rules
Neuro-fuzzy,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy logic,Fuzzy set,Artificial intelligence,Fuzzy associative matrix,Fuzzy number,Machine learning
Conference
Volume
ISSN
Citations 
1
1098-7584
1
PageRank 
References 
Authors
0.38
0
2
Name
Order
Citations
PageRank
Henri Prade1105491445.02
Mathieu Serrurier226726.94